397 research outputs found
Adjacent Graph Based Vulnerability Assessment for Electrical Networks Considering Fault Adjacent Relationships Among Branches
Security issues related to vulnerability assessment in electrical networks are necessary for operators to identify the critical branches. At present, using complex network theory to assess the structural vulnerability of the electrical network is a popular method. However, the complex network theory cannot be comprehensively applicable to the operational vulnerability assessment of the electrical network because the network operation is closely dependent on the physical rules not only on the topological structure. To overcome the problem, an adjacent graph (AG) considering the topological, physical, and operational features of the electrical network is constructed to replace the original network. Through the AG, a branch importance index that considers both the importance of a branch and the fault adjacent relationships among branches is constructed to evaluate the electrical network vulnerability. The IEEE 118-bus system and the French grid are employed to validate the effectiveness of the proposed method.National Natural Science Foundation of China under Grant U1734202National Key Research and Development Plan of China under Grant 2017YFB1200802-12National Natural Science Foundation of China under Grant 51877181National Natural Science Foundation of China under Grant 61703345Chinese Academy of Sciences, under Grant 2018-2019-0
Ground-state properties of the two-site Hubbard-Holstein model: an exact solution
We revisit the two-site Hubbard-Holstein model by using extended phonon
coherent states. The nontrivial singlet bipolaron is studied exactly in the
whole coupling regime. The ground-state (GS) energy and the double occupancy
probability are calculated. The linear entropy is exploited successfully to
quantify bipartite entanglement between electrons and their environment
phonons, displaying a maximum entanglement of the singlet-bipolaron in strong
coupling regime. A dramatic drop in the crossover regime is observed in the GS
fidelity and its susceptibility. The bipolaron properties is also characterized
classically by correlation functions. It is found that the crossover from a
two-site to single-site bipolaron is more abrupt and shifts to a larger
electron-phonon coupling strength as electron-electron Coulomb repulsion
increases.Comment: 6 pages, 6 figure
ME-PCN: Point Completion Conditioned on Mask Emptiness
Point completion refers to completing the missing geometries of an object
from incomplete observations. Main-stream methods predict the missing shapes by
decoding a global feature learned from the input point cloud, which often leads
to deficient results in preserving topology consistency and surface details. In
this work, we present ME-PCN, a point completion network that leverages
`emptiness' in 3D shape space. Given a single depth scan, previous methods
often encode the occupied partial shapes while ignoring the empty regions (e.g.
holes) in depth maps. In contrast, we argue that these `emptiness' clues
indicate shape boundaries that can be used to improve topology representation
and detail granularity on surfaces. Specifically, our ME-PCN encodes both the
occupied point cloud and the neighboring `empty points'. It estimates
coarse-grained but complete and reasonable surface points in the first stage,
followed by a refinement stage to produce fine-grained surface details.
Comprehensive experiments verify that our ME-PCN presents better qualitative
and quantitative performance against the state-of-the-art. Besides, we further
prove that our `emptiness' design is lightweight and easy to embed in existing
methods, which shows consistent effectiveness in improving the CD and EMD
scores.Comment: Accepted to ICCV 2021; typos correcte
Task-Aware Sampling Layer for Point-Wise Analysis
Sampling, grouping, and aggregation are three important components in the
multi-scale analysis of point clouds. In this paper, we present a novel
data-driven sampler learning strategy for point-wise analysis tasks. Unlike the
widely used sampling technique, Farthest Point Sampling (FPS), we propose to
learn sampling and downstream applications jointly. Our key insight is that
uniform sampling methods like FPS are not always optimal for different tasks:
sampling more points around boundary areas can make the point-wise
classification easier for segmentation. Towards this end, we propose a novel
sampler learning strategy that learns sampling point displacement supervised by
task-related ground truth information and can be trained jointly with the
underlying tasks. We further demonstrate our methods in various point-wise
analysis tasks, including semantic part segmentation, point cloud completion,
and keypoint detection. Our experiments show that jointly learning of the
sampler and task brings better performance than using FPS in various
point-based networks.Comment: 14 pages, 13 figures and 14 table
Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction
Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction
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